segformer-b4-wall

This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.1537
• Mean Accuracy: 0.9448
• Mean Iou: 0.8993
• Overall Accuracy: 0.9558
• Per Category Accuracy: [0.9648476610683054, 0.9680509025433003, 0.9015647356112896, nan]
• Per Category Iou: [0.9294668192886654, 0.9344825387850888, 0.8340281823830938, nan]

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 6e-05
• train_batch_size: 64
• eval_batch_size: 64
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Mean Accuracy	Mean Iou	Overall Accuracy	Per Category Accuracy	Per Category Iou
0.1398	5.3476	1000	0.1477	0.9424	0.8733	0.9420	[0.9375947027923643, 0.962438818648652, 0.9270677962243152, nan]	[0.9071928258269675, 0.9154732958813474, 0.7971633247503161, nan]
0.1114	10.6952	2000	0.1329	0.9426	0.8878	0.9498	[0.9551513266050631, 0.9606741248023447, 0.9120448217426163, nan]	[0.9197608920879746, 0.9255854097692368, 0.818153830444766, nan]
0.0683	16.0428	3000	0.1353	0.9473	0.8921	0.9516	[0.9527839457434386, 0.9691455504455139, 0.9198476394516605, nan]	[0.922537499674425, 0.926305870761282, 0.8273726843249476, nan]
0.0753	21.3904	4000	0.1311	0.9437	0.8959	0.9540	[0.9633835386385788, 0.9611760655179852, 0.9066569940696604, nan]	[0.9267602358926313, 0.9312805978213234, 0.8297698871401628, nan]
0.0505	26.7380	5000	0.1397	0.9442	0.8971	0.9545	[0.9627544499461427, 0.967327419780526, 0.9024453947068249, nan]	[0.9272910775593762, 0.9304849186604474, 0.8333807013974415, nan]
0.0427	32.0856	6000	0.1414	0.9455	0.8992	0.9555	[0.9640187847053339, 0.9652081246861538, 0.9074073950598316, nan]	[0.9289147168722637, 0.9321577805497577, 0.8366507705917902, nan]
0.0556	37.4332	7000	0.1477	0.9452	0.8984	0.9552	[0.9629165900233977, 0.9697602413261539, 0.9029026554269718, nan]	[0.9285106797857617, 0.9331322728249959, 0.833620894806762, nan]
0.0424	42.7807	8000	0.1484	0.9439	0.8990	0.9557	[0.9653151526182964, 0.96949089540134, 0.8967977175922358, nan]	[0.9292691886525306, 0.9343666443212755, 0.83323737535253, nan]
0.053	48.1283	9000	0.1537	0.9448	0.8993	0.9558	[0.9648476610683054, 0.9680509025433003, 0.9015647356112896, nan]	[0.9294668192886654, 0.9344825387850888, 0.8340281823830938, nan]

Framework versions

• Transformers 4.40.2
• Pytorch 2.3.0+cu121
• Datasets 2.17.0
• Tokenizers 0.19.1